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Journal articles on the topic 'Audio Forensics'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Fayyad-Kazan, Hasan, Ale Hejase, Imad Moukadem, and Sondos Kassem-Moussa. "Verifying the Audio Evidence to Assist Forensic Investigation." Computer and Information Science 14, no. 3 (2021): 25. http://dx.doi.org/10.5539/cis.v14n3p25.

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Audio forensics is a field in forensics that is used to authenticate, enhance, and analyze audio files to aid in solving different crime investigations. Audio as a forensic evidence must be enhanced and analyzed to be admissible in courts of law. But more importantly, it must be authenticated in order to prove that it is authentic and no manipulations were done to it. In this paper, an overview on audio forensics is presented, previous related work to this topic is shown, and methodologies for audio enhancement and authentication are explained along with audio tampering ways and signatures pre

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Tao, Biaoli, Rangding Wang, Diqun Yan, and Chao Jin. "Anti-Forensics of Double Compressed MP3 Audio." International Journal of Digital Crime and Forensics 12, no. 3 (2020): 45–57. http://dx.doi.org/10.4018/ijdcf.2020070104.

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The widespread availability of audio editing software has made it easy to create acoustically convincing digital audio forgeries. To address this problem, more and more attention has been paid to the field of digital audio forensics. There has been little work, however, in the field of anti-forensics, which seeks to develop a set of techniques designed to fool current forensic methodologies. The compression history of an audio sample can be used to provide evidence of audio forgeries. In this work, we present a simple method for distinguishing the MP3 compression history of an audio sample. We

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3

Sunardi, Sunardi, Imam Riadi, Rusydi Umar, and Muhammad Fauzan Gustafi. "Audio Forensics on Smartphone with Digital Forensics Research Workshop (DFRWS) Method." CommIT (Communication and Information Technology) Journal 15, no. 1 (2021): 41–47. http://dx.doi.org/10.21512/commit.v15i1.6739.

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Audio is one of the digital items that can reveal a happened case. However, audio evidence can also be manipulated and changed to hide information. Forensics audio is a technique to identify the sound’s owner from the audio using pitch, formant, and spectrogram parameters. The conducted research examines the similarity of the original sound with the manipulated voice to determine the owner of the sound. It analyzes the level of similarity or identical sound using spectrogram analysis with the Digital Forensics Research Workshop (DFRWS) Method. The research objects are original and manipulated

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Qamhan, Mustafa, Yousef A. Alotaibi, and Sid-Ahmed Selouani. "Source Microphone Identification Using Swin Transformer." Applied Sciences 13, no. 12 (2023): 7112. http://dx.doi.org/10.3390/app13127112.

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Microphone identification is a crucial challenge in the field of digital audio forensics. The ability to accurately identify the type of microphone used to record a piece of audio can provide important information for forensic analysis and crime investigations. In recent years, transformer-based deep-learning models have been shown to be effective in many different tasks. This paper proposes a system based on a transformer for microphone identification based on recorded audio. Two types of experiments were conducted: one to identify the model of the microphones and another in which identical m

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Ghane, Mohamed C., Maider D. Uribarri, Ramzi Djemai, Dipo Dunsin, and Istteffanny I. Araujo. "A Novel Hybrid Method for Effective Identification and Extraction of Digital Evidence Masked by Steganographic Techniques in WAV and MP3 Files." Journal of Information Security and Cybercrimes Research 6, no. 2 (2023): 89–104. http://dx.doi.org/10.26735/izbk9372.

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Anti-forensics techniques, particularly steganography and cryptography, have become increasingly pressing issues affecting current digital forensics practices. This paper advances the automation of hidden evidence extraction in audio files by proposing a novel multi-approach method. This method facilitates the correlation between unprocessed artefacts, indexed and live forensics analysis, and traditional steganographic and cryptographic detection techniques. In this work, we opted for experimental research methodology in the form of a quantitative analysis of the efficiency of the proposed aut

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N, Rahmawati. "ANALISIS STATISTIK PITCH PADA AUDIO FORENSIK UNTUK BUKTI DIGITAL." Cyber Security dan Forensik Digital 5, no. 2 (2023): 66–70. http://dx.doi.org/10.14421/csecurity.2022.5.2.2345.

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Teknologi informasi dan komputer terus berkembang dan telah dimanfaatkan secara luas maupun mendalam. Komputer/digital forensik merupkan aplikasi dari teknologi komputer untuk pembuktian hukum terkait dengan kejahatan berteknologi tinggi hingga bisa mendapatkan bukti bukti digital untuk menjerat para pelaku kejahatan. Wilayah forensik digital cukup luas karena jenis dari konten digital itu sendiri ada banyak, seperti website, email, gambar, audio, video, dan lain-lain. Pada kasus pidana maupun perdata diperlukan barang bukti, salah satunya adalah barang bukti digital/ elektronik. Barang bukti

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7

Muhammad, Ghulam, and Khalid Alghathbar. "Environment Recognition for Digital Audio Forensics Using MPEG-7 and MEL Cepstral Features." Journal of Electrical Engineering 62, no. 4 (2011): 199–205. http://dx.doi.org/10.2478/v10187-011-0032-0.

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Environment Recognition for Digital Audio Forensics Using MPEG-7 and MEL Cepstral FeaturesEnvironment recognition from digital audio for forensics application is a growing area of interest. However, compared to other branches of audio forensics, it is a less researched one. Especially less attention has been given to detect environment from files where foreground speech is present, which is a forensics scenario. In this paper, we perform several experiments focusing on the problems of environment recognition from audio particularly for forensics application. Experimental results show that the

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Dzulfikar, Helmy, Sisdarmanto Adinandra, and Erika Ramadhani. "The Comparison of Audio Analysis Using Audio Forensic Technique and Mel Frequency Cepstral Coefficient Method (MFCC) as the Requirement of Digital Evidence." Jurnal Online Informatika 6, no. 2 (2021): 145. http://dx.doi.org/10.15575/join.v6i2.702.

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Audio forensics is the application of science and scientific methods in handling digital evidence in the form of audio. In this regard, the audio supports the disclosure of various criminal cases and reveals the necessary information needed in the trial process. So far, research related to audio forensics is more on human voices that are recorded directly, either by using a voice recorder or voice recordings on smartphones, which are available on Google Play services or iOS Store. This study compares the analysis of live voices (human voices) with artificial voices on Google Voice and other ar

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Voznyuk, M. A., and Yu A. Denisov. "Forensic Diagnostics of the Circumstances of Digital Video and Audio Production: Analytical Review." Theory and Practice of Forensic Science 12, no. 1 (2017): 48–71. http://dx.doi.org/10.30764/64/1819-2785-2017-12-1-48-71.

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The analytical review examines the issues of forensic investigation into the circumstances of production of digital video and audio recordings. The paper presents the organization and lines of inquiry in forensic analysis of video and audio evidence, computer forensics, forensic linguistics, and psychological evaluation established in the system of forensic science organizations of the Russian Ministry of Justice by 2013–2016. The analysis covers the issues of independent or integrated application of the listed types of special knowledge in order to meet the discipline-specific and comprehensi

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Sonali, Ekhande, Patil Uttam, and Vishwanath Kulhalli Kshama. "Review on effectiveness of deep learning approach in digital forensics." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 5 (2022): 5481–92. https://doi.org/10.11591/ijece.v12i5.pp5481-5492.

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Cyber forensics is use of scientific methods for definite description of cybercrime activities. It deals with collecting, processing and interpreting digital evidence for cybercrime analysis. Cyber forensic analysis plays very important role in criminal investigations. Although lot of research has been done in cyber forensics, it is still expected to face new challenges in near future. Analysis of digital media specifically photographic images, audio and video recordings are very crucial in forensics This paper specifically focus on digital forensics. There are several methods for digital fore

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Ashu, Goyal, K. Shukla S, and K. Sarin R. "A comparative study of audio latency feature of Motorola and Samsung mobile phones in forensic identification." Indian Journal of Science and Technology 14, no. 4 (2021): 319–24. https://doi.org/10.17485/IJST/v14i4.2108.

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Abstract <strong>Background:</strong> In forensic science the process of proving authenticity of audio recording plays an important role. In recent times, Forensic experts mostly receives digital recording for authentication as compared to analog recording. A digitally altered audio signal, leaves no visual indications of being tampered, and it will be indistinguishable from an original audio signal. Objective: To highlight the significance of latency feature of mobile phone handsets in forensic science via comparing input audio latency feature of Samsung and Motorola mobile phone in two

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Mardlatillah, Hanifah, Yudi Prayudi, and Erika Ramadhani. "Anti Forensik Voice Note Menggunakan Whatsapp Mod." Jurnal Teknik 21, no. 2 (2023): 239–55. http://dx.doi.org/10.37031/jt.v21i2.328.

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Currently there is a social media application that provides additional features in the form of voice changers by utilizing the voice note feature of Whatsapp Mod which can blur the identity of the voice owner. In several articles, Whatsapp Mod claims that this application is an anti-banned application, so it is feared that Whatsapp Mod is an application that uses anti-forensic techniques in it. This study aims to prove whether voice modifiers using voice notes on Whatsapp Mod can be a form of anti-forensic activity which can later make it difficult for investigators to conduct investigations.

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Wisdom, Frey John. "Digital Forensics In Multimedia." Advances in Multidisciplinary and scientific Research Journal Publication 1, no. 1 (2022): 239–50. http://dx.doi.org/10.22624/aims/crp-bk3-p39.

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Digital forensics and multimedia forensics are rapidly growing disciplines where electronic information is extracted and interpreted using scientifically accepted and validated processes, to be used in and outside of a court of law. As personal computing and the internet becomes more widespread, these two fields are becoming increasingly important in law enforcement and cybercrime investigation.Digital forensics involves investigating computer systems and digital artefacts in general, while multimedia forensics is a sub-topic of digital forensics which focuses on extracting and analyzing conte

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Khan, Muhammad Khurram, Mohammed Zakariah, Hafiz Malik, and Kim-Kwang Raymond Choo. "A novel audio forensic data-set for digital multimedia forensics." Australian Journal of Forensic Sciences 50, no. 5 (2017): 525–42. http://dx.doi.org/10.1080/00450618.2017.1296186.

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Kang, Haein, Hojun Seong, Ilkyu Kim, et al. "Android-Based Audio Video Navigation System Forensics: A Case Study." Applied Sciences 13, no. 10 (2023): 6176. http://dx.doi.org/10.3390/app13106176.

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Vehicle digital forensics includes the process of collecting and analysing digital data stored in vehicles to find evidence related to traffic accidents or crime scenes. Through this process, we can reconstruct digital events by recognizing various information such as driver behaviour, driving patterns, vehicle destinations, and smartphones connected to a vehicle. Recently, many vehicle digital forensic studies have been conducted, but few of them have dealt with Android-based infotainment (or audio video navigation, AVN) systems. While many AVN systems adopt Android as the operating system, d

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Ekhande, Sonali, Uttam Patil, and Kshama Vishwanath Kulhalli. "Review on effectiveness of deep learning approach in digital forensics." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 5 (2022): 5481. http://dx.doi.org/10.11591/ijece.v12i5.pp5481-5592.

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<p><span>Cyber forensics is use of scientific methods for definite description of cybercrime activities. It deals with collecting, processing and interpreting digital evidence for cybercrime analysis. Cyber forensic analysis plays very important role in criminal investigations. Although lot of research has been done in cyber forensics, it is still expected to face new challenges in near future. Analysis of digital media specifically photographic images, audio and video recordings are very crucial in forensics This paper specifically focus on digital forensics. There are several met

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Kvíčalová, Anna. "Other than Ethical: STS-Oriented Approaches to Communist Audio Forensics." Prace Kulturoznawcze 26, no. 1 (2022): 121–27. http://dx.doi.org/10.19195/0860-6668.26.1.7.

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This article considers the benefits of constructivist approaches to the history of audio forensics. It is argued that science and technology studies (STS) open up a new avenue of research on historical uses of sound recordings in the communist security apparatus and offer a perspective that is considerably different from the mainstream historiographical treatment of the state audio surveillance. This claim serves as a basis for discussing the Czechoslovak programme of audio forensics (1975–1989).

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Jiang, Yi, and Dengpan Ye. "Black-Box Adversarial Attacks against Audio Forensics Models." Security and Communication Networks 2022 (January 17, 2022): 1–8. http://dx.doi.org/10.1155/2022/6410478.

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Speech synthesis technology has made great progress in recent years and is widely used in the Internet of things, but it also brings the risk of being abused by criminals. Therefore, a series of researches on audio forensics models have arisen to reduce or eliminate these negative effects. In this paper, we propose a black-box adversarial attack method that only relies on output scores of audio forensics models. To improve the transferability of adversarial attacks, we utilize the ensemble-model method. A defense method is also designed against our proposed attack method under the view of the

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Bintang, Rauhulloh Ayatulloh, Rusydi Umar, and Anton Yudhana. "Analisis Media Sosial Facebook Lite dengan tools Forensik menggunakan Metode NIST." Techno (Jurnal Fakultas Teknik, Universitas Muhammadiyah Purwokerto) 21, no. 2 (2020): 125. http://dx.doi.org/10.30595/techno.v21i2.8494.

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Social Media is becoming very popular among the public today, and the increasing number of social media use has of course a good or bad impact on the course of human life, for example the bad impact is doing cyberbully or chating on social media. Digital forensics is one of the sciences for how to catch criminals in digital which will be needed in evidence in court. Social media criminals need Smartphones to commit digital cybercrime. This research will raise evidence of digital crimes on the Facebook Lite application using forensics. In this study, the forensic tool that will be used is the M

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Luo, Weiqi, Haodong Li, Qi Yan, Rui Yang, and Jiwu Huang. "Improved Audio Steganalytic Feature and Its Applications in Audio Forensics." ACM Transactions on Multimedia Computing, Communications, and Applications 14, no. 2 (2018): 1–14. http://dx.doi.org/10.1145/3190575.

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Renza, Diego, Jaisson Vargas, and Dora M. Ballesteros. "Robust Speech Hashing for Digital Audio Forensics." Applied Sciences 10, no. 1 (2019): 249. http://dx.doi.org/10.3390/app10010249.

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The verification of the integrity and authenticity of multimedia content is an essential task in the forensic field, in order to make digital evidence admissible. The main objective is to establish whether the multimedia content has been manipulated with significant changes to its content, such as the removal of noise (e.g., a gunshot) that could clarify the facts of a crime. In this project we propose a method to generate a summary value for audio recordings, known as hash. Our method is robust, which means that if the audio has been modified slightly (without changing its significant content

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O. A., Leonenko, Yesypenko O. H., and Rakhilchuk І.V. "Modern legal and organizational principles for the development of judicial examination in crime investigation." Scientific Herald of Sivershchyna. Series: Law 2020, no. 2 (2020): 93–101. http://dx.doi.org/10.32755/sjlaw.2020.02.093.

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The scientific article is devoted to the problems of using forensic examinations and their results in the pre-trial investigation. Perspective directions of development of criminology according to its components are defined. The current state of develop-ment of forensic technology, tools and cybernetics in general, which is successfully used in forensics makes it possible to use retinal schemes to register criminals. It is noted that the development of forensic technologies involves the develop-ment of information retrieval programs, such as Automated Workplaces (AWP) for pre-trial investigati

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Rahmat, Inggi, Sugiantoro Bambang, and Prayudi Yudi. "PENERAPAN SYSTEM DEVELOPMENT LIFE CYCLE (SDLC)." semanTIK 4, no. 2 (2018): 193–200. https://doi.org/10.5281/zenodo.2528444.

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<strong><em>Abstract</em></strong> <em>An audio recorder is one of the multimedia content that is often found at the crime scene and is used as evidence in the trial process. Audio recorders that can be made as legal evidence in court must follow the rules and audio forensic standards that apply in the process of obtaining the evidence, this is because audio digital evidence is very easy to manipulate. The forensic audio investigation process requires a framework that can be used as a reference in the investigation process. However, at present, the developing audio forensic focus more on analy

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Huang, Xinchao, Zihan Liu, Wei Lu, Hongmei Liu, and Shijun Xiang. "Fast and Effective Copy-Move Detection of Digital Audio Based on Auto Segment." International Journal of Digital Crime and Forensics 11, no. 2 (2019): 47–62. http://dx.doi.org/10.4018/ijdcf.2019040104.

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Detecting digital audio forgeries is a significant research focus in the field of audio forensics. In this article, the authors focus on a special form of digital audio forgery—copy-move—and propose a fast and effective method to detect doctored audios. First, the article segments the input audio data into syllables by voice activity detection and syllable detection. Second, the authors select the points in the frequency domain as feature by applying discrete Fourier transform (DFT) to each audio segment. Furthermore, this article sorts every segment according to the features and gets a sorted

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Ardiningtias, Syifa Riski Ardiningtias, Sunardi Sunardi, and Herman Herman. "INVESTIGASI DIGITAL PADA FACEBOOK MESSENGER MENGGUNAKAN NATIONAL INSTITUTE OF JUSTICE." Jurnal Informatika Polinema 7, no. 4 (2021): 19–26. http://dx.doi.org/10.33795/jip.v7i4.709.

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Perkembangan teknologi memudahkan masyarakat dalam berbagi informasi dan berkomunikasi. Facebook Messenger merupakan salah satu instant messanger yang memiliki kelebihan multi-platform yang dapat digunakan oleh pengguna dalam pengiriman pesan teks, gambar, pesan suara, dan video. Selain digunakan sebagai hal untuk kegiatan positif, namun fasilitas dalam teknologi ini juga dapat digunakan untuk melakukan kegiatan negatif. Penelitian ini melakukan investigasi forensik pada simulasi adanya tindakan kejahatan dalam penyebaran konten pornografi menggunakan Facebook Messenger sebagai media komunikas

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Nour Mohammad, Hasan Fayyad-Kazan, Mohamad Saab. "Anti- Forensics: The Tampering of Media." International Journal on Recent and Innovation Trends in Computing and Communication 8, no. 10 (2020): 06–30. http://dx.doi.org/10.17762/ijritcc.v8i10.5447.

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In the context of forensic investigations, the traditional understanding of evidence is changing where nowadays most prosecutors, lawyers and judges heavily rely on multimedia signs. This modern shift has allowed the law enforcement to better reconstruct the crime scenes or reveal the truth of any critical event.In this paper we shed the light on the role of video, audio and photos as forensic evidences presenting the possibility of their tampering by various easy-to-use, available anti-forensics softwares. We proved that along with the forensic analysis, digital processing, enhancement and au

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Ali, Zulfiqar, Muhammad Imran, and Mansour Alsulaiman. "An Automatic Digital Audio Authentication/Forensics System." IEEE Access 5 (2017): 2994–3007. http://dx.doi.org/10.1109/access.2017.2672681.

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Naik, Deepak. "Fake Media Forensics:AI – Driven Forensic Analysis of Fake Multimedia Content." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47208.

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Abstract—With the rapid advancement of deep learning techniques, the generation of synthetic media—commonly Research and development on deepfakes technology have reached new levels of sophistication. Digital security along with misinformation face serious threats because of these sophisticated methods. and privacy. Existing deepfake detection models primarily the detection methods primarily analyze either video or audio or image-based forgeries yet they seldom employ unified multi-modal examination methods. The authors introduce here a multi-modal deepfake detection system. The proposed framew

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Renza, Diego, Jaime Andres Arango, and Dora Maria Ballesteros. "A Mobile-Oriented System for Integrity Preserving in Audio Forensics." Applied Sciences 9, no. 15 (2019): 3097. http://dx.doi.org/10.3390/app9153097.

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This paper addresses a problem in the field of audio forensics. With the aim of providing a solution that helps Chain of Custody (CoC) processes, we propose an integrity verification system that includes capture (mobile based), hash code calculation and cloud storage. When the audio is recorded, a hash code is generated in situ by the capture module (an application), and it is sent immediately to the cloud. Later, the integrity of the audio recording given as evidence can be verified according to the information stored in the cloud. To validate the properties of the proposed scheme, we conduct

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Kurniawan, Fajri, Mohd Shafry Mohd. Rahim, Mohammed S. Khalil, and Muhammad Khurram Khan. "Statistical Based Audio Forensic on Identical Microphones." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 5 (2016): 2211. http://dx.doi.org/10.11591/ijece.v6i5.12022.

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<p>Microphone forensics has become a challenging field due to the proliferation of recording devices and explosion in video/audio recording. Video or audio recording helps a criminal investigator to analyze the scene and to collect evidences. In this regards, a robust method is required to assure the originality of some recordings. In this paper, we focus on digital audio forensics and study how to identify the microphone model. Defining microphone model will allow the investigators to conclude integrity of some recordings. We perform statistical analysis on the recording that is collect

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Kurniawan, Fajri, Mohd Shafry Mohd. Rahim, Mohammed S. Khalil, and Muhammad Khurram Khan. "Statistical Based Audio Forensic on Identical Microphones." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 5 (2016): 2211. http://dx.doi.org/10.11591/ijece.v6i5.pp2211-2218.

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<p>Microphone forensics has become a challenging field due to the proliferation of recording devices and explosion in video/audio recording. Video or audio recording helps a criminal investigator to analyze the scene and to collect evidences. In this regards, a robust method is required to assure the originality of some recordings. In this paper, we focus on digital audio forensics and study how to identify the microphone model. Defining microphone model will allow the investigators to conclude integrity of some recordings. We perform statistical analysis on the recording that is collect

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Ruslan, Takdir, Imam Riadi, and Sunardi Sunardi. "FORENSIK MULTIMEDIA BERBASIS MOBILE MENGGUNAKAN METODE NATIONAL INSTITUTE OF JUSTICE." Jurnal SAINTEKOM 12, no. 1 (2022): 69–80. http://dx.doi.org/10.33020/saintekom.v12i1.210.

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Current technological developments make it easier for people to share information and carry out communication activities through instant messaging applications using multimedia features in the form of audio and video. Technology has been able to contribute and facilitate humans in a positive sense, but it can also be misused to carry out negative activities. In solving crime problems using instant messages, investigators need to carry out digital forensics, including on mobile devices such as smartphones. This study aims to obtain digital evidence of online drug sales scenarios via Facebook me

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Williams, Emily L., Karl O. Jones, Colin Robinson, Sebastian Chandler Crnigoj, Helen Burrell, and Suzzanne McColl. "How Frequency and Harmonic Profiling of a ‘Voice’ Can Inform Authentication of Deepfake Audio: An Efficiency Investigation." Journal of Advances in Engineering and Technology 3, no. 1 (2025): 49–58. https://doi.org/10.54389/hgbc7543.

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As life in the digital era becomes more complex, the capacity for criminal activity within the digital realm becomes even more widespread. More recently, the development of deepfake media generation powered by Artificial Intelligence pushes audio and video content into a realm of doubt, misinformation, or misrepresentation. The instances of deepfake videos are numerous, with some infamous cases ranging from manufactured graphic images of the musician Taylor Swift, through to the loss of $25 million dollars transferred after a faked video call. The problems of deepfake are becoming increasingly

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Patole, Rashmika, and Priti P. Rege. "Machine Learning–Based Splicing Detection in Digital Audio Recordings for Audio Forensics." Journal of the Audio Engineering Society 69, no. 11 (2021): 793–804. http://dx.doi.org/10.17743/jaes.2021.0044.

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Renza, Diego, Dora M. Ballesteros L., and Camilo Lemus. "Authenticity verification of audio signals based on fragile watermarking for audio forensics." Expert Systems with Applications 91 (January 2018): 211–22. http://dx.doi.org/10.1016/j.eswa.2017.09.003.

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Subki, Ahmad, Muh Nasirudin Karim, and Bahtiar Imran. "Analisis Rekaman Suara pada Aplikasi Magic Call dengan Metode Forensik Audio untuk Mendapatkan Bukti Digital." Jurnal SAINTEKOM 13, no. 2 (2023): 111–22. http://dx.doi.org/10.33020/saintekom.v13i2.373.

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Audio forensics is a method used to analyze sound or audio recordings. Voice or audio recording is one of the digital evidence that is easy to manipulate. One way to manipulate sound is to use magic call. Magic call has several levels of character voices that can be used such as cartoon, children, male and female voices. The analysis of the original voice recording with the magic voice recording is done by comparing the magic call sound and the voice with the original voice recording. The purpose of this study was to determine the voice recording produced by magic call from the magic call appl

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Zakariah, Mohammed, Muhammad Khurram Khan, and Hafiz Malik. "Digital multimedia audio forensics: past, present and future." Multimedia Tools and Applications 77, no. 1 (2017): 1009–40. http://dx.doi.org/10.1007/s11042-016-4277-2.

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Seo, Young-Eun, Sang-Geun Ahn, and Hae-Yeoun Lee. "MFCC-based Audio Quality Classification Method for Forensics." Journal of Korean Institute of Information Technology 22, no. 1 (2024): 131–38. http://dx.doi.org/10.14801/jkiit.2024.22.1.131.

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Venkatesh, M. "A Novel Digital Audio Encryption and Watermarking Scheme." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem.ncft044.

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Abstract—To enhance the privacy and security of audio signals stored in third-party storage centers, a robust digital audio encryption and forensics watermarking scheme is proposed. The scheme incorporates the AES-GCM (Advanced Encryption Standard in Galois/Counter Mode) algorithm for authenticated encryption, ensuring both confidentiality and integrity of the audio data. In addition, we utilize Fernet symmetric encryption and PBKDF2HMAC (Password-Based Key Derivation Function 2 with HMAC) for key generation, supported by generative hash passwords to further strengthen security. The signal ene

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Venkatesh, M. "A Novel Digital Audio Encryption and Watermarking Scheme." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem.ncft043.

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Abstract—To enhance the privacy and security of audio signals stored in third-party storage centers, a robust digital audio encryption and forensics watermarking scheme is proposed. The scheme incorporates the AES-GCM (Advanced Encryption Standard in Galois/Counter Mode) algorithm for authenticated encryption, ensuring both confidentiality and integrity of the audio data. In addition, we utilize Fernet symmetric encryption and PBKDF2HMAC (Password-Based Key Derivation Function 2 with HMAC) for key generation, supported by generative hash passwords to further strengthen security. The signal ene

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Son, Homin, Sung Won Baek, and Jae Wan Park. "Automated Detection of Container-based Audio Forgery Using Crowdsourcing for Dataset Building." Aptisi Transactions on Technopreneurship (ATT) 6, no. 1 (2024): 119–35. http://dx.doi.org/10.34306/att.v6i1.383.

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This research paper introduces a new approach for detecting digital audio file forgeries, highlighting entrepreneurship spirit through innovation and market adaptability. It utilizes a cyclical system aided by mobile crowdsourcing to collect a comprehensive dataset of smartphone recordings, demonstrating an entrepreneurship approach to leveraging technology for scalable solutions. Digital forgery, an area ripe for entrepreneurship investment, is becoming increasingly accessible, making it challenging to detect manipulated audio files. This new method emphasizes the utility of metadata and file

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Nimbalkar, Suhas. "“Deepfake Detection in Call Recordings:A Deep Learning Solution for Voice Authentication”." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem49194.

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Abstract— The emergence of deepfake technology has improved exponentially and this intensified the fears that surround the credibility of audio recordings, in instance telecommunication and security. This project proposes a full deep learning-based approach to deepfake voice recordings detection in call communications as an improvement to the voice authentication processes used. It is with this in mind that we came up with an adaptive architecture that positions convolutional neural networks (CNN) and recurrent neural network (RNN) in a way that assists in distinguishing between real and fabri

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Dhishan, Dhammearatchi. "Use of Network Forensic Mechanisms to Formulate Network Security." Use of Network Forensic Mechanisms to Formulate Network Security 7, no. 4 (2015): 21 to 35. https://doi.org/10.5281/zenodo.3839184.

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Network Forensics is fairly a new area of research which would be used after an intrusion in various organizations ranging from small, mid-size private companies and government corporations to the defence secretariat of a country. At the point of an investigation valuable information may be mishandled which leads to difficulties in the examination and time wastage. Additionally the intruder could obliterate tracks such as intrusion entry, vulnerabilities used in an entry, destruction caused, and most importantly the identity of the intruder. The aim of this research was to map the correlation

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A. Srinagesh, Venkateswarlu Sunkari,. "Efficient Deepfake Audio Detection Using Spectro-Temporal Analysis and Deep Learning." Journal of Electrical Systems 20, no. 5s (2024): 10–18. http://dx.doi.org/10.52783/jes.1829.

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With the advancement of deepfake technology, particularly in the audio domain, there is an imperative need for robust detection mechanisms to maintain digital security and integrity. Our research presents an "Enhanced Deepfake Audio Detection with Spectro-Temporal Deep Learning Approach," aimed at addressing the escalating challenge of distinguishing genuine audio from sophisticated deepfake manipulations. Our methodology leverages the ADD2022 dataset, which encompasses a wide range of audio clips, to train and evaluate a novel deep learning model. The core of our approach consists of a meticu

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Altulaihan, Esra, Abrar Alismail, M. M. Hafizur Rahman, and Adamu A. Ibrahim. "Email Security Issues, Tools, and Techniques Used in Investigation." Sustainability 15, no. 13 (2023): 10612. http://dx.doi.org/10.3390/su151310612.

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The email system is a globally distributed communication infrastructure service that involves multiple actors playing different roles to ensure end-to-end mail delivery. It is an indispensable method of communicating that is changing how people share data and information. As a result, it facilitates effective and efficient communication, especially in business, as well as convenience, accessibility, and replication. Today, email can send more than just text files; it can also send audio, video, photos, and other files with extensions. With email becoming ubiquitous in all aspects of our lives,

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Wicaksono, Hafizh Enggar Kuswiharso, Niken Dwi Wahyu Cahyani, and Vera Suryani. "ANALYSIS OF THE IMPACT OF DISTORTION ON SOUND RECORDINGS AS ANTI FORENSIC ACTIVITIES." JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) 8, no. 1 (2023): 140–53. http://dx.doi.org/10.29100/jipi.v8i1.3331.

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Anti-forensics on audio is aimed at complicating investigations on audio forensics, on sound recordings. Sound recordings can be altered or manipulated in various ways as well as the provision of distortion effects on sound recordings. Effect such distortions will make it difficult for investigators to find out the owner of the original voice. Analysis of distortion effects on sound recordings for anti-forensic activities, has not been widely carried out. Distortion can be an effective anti-forensic technique because the sound produced will be noisy, making it difficult for investigators to co

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Eom, So-Jeong, Hyun-Soo Kim, and Hae-Yeoun Lee. "Audio Forensics for Smartphone Recording Detection using Deep Learning." Journal of Korean Institute of Information Technology 20, no. 7 (2022): 103–9. http://dx.doi.org/10.14801/jkiit.2022.20.7.103.

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Malik, Hafiz. "Acoustic Environment Identification and Its Applications to Audio Forensics." IEEE Transactions on Information Forensics and Security 8, no. 11 (2013): 1827–37. http://dx.doi.org/10.1109/tifs.2013.2280888.

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Camacho, Steven, Dora M. Ballesteros L, and Diego Renza. "A cloud-oriented integrity verification system for audio forensics." Computers & Electrical Engineering 73 (January 2019): 259–67. http://dx.doi.org/10.1016/j.compeleceng.2018.11.022.

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Luo, Da, Pawel Korus, and Jiwu Huang. "Band Energy Difference for Source Attribution in Audio Forensics." IEEE Transactions on Information Forensics and Security 13, no. 9 (2018): 2179–89. http://dx.doi.org/10.1109/tifs.2018.2812185.

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